How to Use the Geekflare MCP in Pydantic AI

Bring type-safe domain auditing to Pydantic AI with this MCP Server.

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Connect Geekflare MCP to Pydantic AI

Create your Vinkius account to connect Geekflare to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Geekflare with Pydantic AI

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Strictly typed audits for Pydantic AI

Run `run_lighthouse_audit` or `scan_ssl_tls_cert` and get responses validated against your Pydantic schemas. It stops bad data from propagating through your agent's logic. If the audit returns an unexpected field, the agent fails immediately. You get full control over data integrity.

Domain record lookup for Pydantic AI

Use `get_dns_records` and `get_whois_data` to populate your typed models. Your agent works with structured, verified records rather than raw strings. It makes your code cleaner and significantly reduces the chance of logic errors when parsing complex network configuration data.

Performance verification in Pydantic AI

Execute `measure_load_time` and `check_broken_links` to verify site metrics. The results are cast into your models, allowing for precise conditional logic based on performance. `take_website_screenshot` provides the visual context your agent needs to confirm that your site is rendering correctly across different environments.

Setup guide

Set up Geekflare MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "geekflare-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Geekflare tools.",
)

result = await agent.run("List recent Geekflare transactions")
print(result.output)

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Common questions about Geekflare MCP in Pydantic AI

The MCP integration automatically maps tool outputs to your models. Any mismatch triggers an immediate validation error, ensuring your data is always correct.

Yes, the server runs externally and communicates via HTTP. You point your Pydantic AI toolset to the endpoint and start auditing.

Every tool output is defined, allowing you to create strict Pydantic schemas that match the exact diagnostic data returned by the server.

It's straightforward. You install the slim package, define your toolset, and pass it to your agent instance.

Your scan results are processed in an ephemeral environment. We don't log your domain audits, and each request is strictly isolated.

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